Entity detection

ABSTRACT

A computer-implemented method for entity detection is described. In one embodiment, an entity passing through a perimeter of a predefined area is detected via a camera. Upon detecting the entity passing through the perimeter of the predefined area, a type of the entity is classified from an image of the entity captured by the camera. Upon classifying the type of the entity, a feature of the entity is detected from the image of the entity. An identifier is assigned to the entity based on the type and the detected feature of the entity. The identifier distinguishes the entity from another entity of a same type.

CROSS REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/589,730 entitled “ENTITY DETECTION,” filed May 8, 2017, which is acontinuation of U.S. patent application Ser. No. 14/560,585 entitled“ENTITY DETECTION,” which was filed 4 Dec. 2014 and which claims thebenefit of U.S. Provisional Patent Application No. 61/912,947 entitled“ENTITY DETECTION,” which was filed 6 Dec. 2013, which are assigned tothe assignee hereof. The disclosures of each of which are incorporatedby reference herein in their entireties.

BACKGROUND

Advancements in media delivery systems and media-related technologiescontinue to increase at a rapid pace. Increasing demand for media hasinfluenced the advances made to media-related technologies. Computersystems have increasingly become an integral part of the media-relatedtechnologies. Computer systems may be used to carry out severalmedia-related functions. The wide-spread access to media has beenaccelerated by the increased use of computer networks, including theInternet and cloud networking.

Many homes and businesses use one or more computer networks to generate,deliver, and receive data and information between the various computersconnected to computer networks. Users of computer technologies continueto demand increased access to information and an increase in theefficiency of these technologies. Improving the efficiency of computertechnologies is desirable to those who use and rely on computers.

With the wide-spread use of computers and mobile devices has come anincreased presence of home automation and home security products.Advancements in mobile devices allow users to monitor a home orbusiness. These devices may provide tracking capabilities of people,vehicles, etc. Conventional devices that track/count different entities,however, do not provide accurate information.

SUMMARY

According to at least one embodiment, a computer-implemented method forentity detection is described. In one embodiment, an entity passingthrough a perimeter of a predefined area may be detected via a camera.Upon detecting the entity passing through the perimeter of thepredefined area, a type of the entity may be classified from an image ofthe entity captured by the camera. Upon classifying the type of theentity, a feature of the entity may be detected from the image of theentity. An identifier may be assigned to the entity based on the typeand the detected feature of the entity. The identifier may distinguishthe entity from another entity of a same type.

In one embodiment, how many entities of the same type as the entity arelocated in the predefined area may be determined. A length of stay ofthe entity at the predefined area may be determined based on a detectedtime of arrival of the entity at the predefined area and a detected timeof departure of the entity from the predefined area. The entity may berecognized based on the detected feature of the entity and whether theentity was assigned an identifier on a previous visit by the entity tothe predefined area may be determined.

In one embodiment, an average length of stay of the entity at thepredefined area may be determined based on the determined length of stayof the entity and a previously determined length of stay determined fromthe previous visit by the entity to the predefined area. Upondetermining the entity was assigned an identifier on a previous visit,how many previous visits the entity has made to the predefined area maybe determined. Upon determining how many previous visits the entity hasmade to the predefined area, an average rate of visits over apredetermined time period may be determined. A transaction by the entityat the predefined area may be associated with the identifier of theentity. An average value of transactions may be determined from thetransaction and previous transactions associated with the identifier ofthe entity.

In one embodiment, the type of the entity may be identified as avehicle, the predefined area being a parking lot. A type of a secondentity exiting the vehicle may be identified as a human. A location ofthe second entity may be tracked and monitored, and whether the secondentity enters a second predefined area may be determined. A notificationmay be generated in response to detecting one or more of the featuresdescribed above.

A computing device configured to obscure content on a screen is alsodescribed. The device may include a processor and memory in electroniccommunication with the processor. The memory may store instructions thatmay be executable by the processor to detect, via a camera, an entitypassing through a perimeter of a predefined area and classify, upondetecting the entity passing through the perimeter of the predefinedarea, a type of the entity from an image of the entity captured by thecamera. The memory may store instructions that may be executable by theprocessor to detect, upon classifying the type of the entity, a featureof the entity from the image of the entity, and assign an identifier tothe entity based at least in part on the type and the detected featureof the entity. The identifier may distinguish the entity from anotherentity of a same type.

A computer-program product to obscure content on a screen is alsodescribed. The computer-program product may include a non-transitorycomputer-readable medium that stores instructions. The instructions maybe executable by the processor to detect, via a camera, an entitypassing through a perimeter of a predefined area and classify, upondetecting the entity passing through the perimeter of the predefinedarea, a type of the entity from an image of the entity captured by thecamera. The memory may store instructions that may be executable by theprocessor to detect, upon classifying the type of the entity, a featureof the entity from the image of the entity, and assign an identifier tothe entity based at least in part on the type and the detected featureof the entity. The identifier may distinguish the entity from anotherentity of a same type.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the general principlesdescribed herein. These and other embodiments, features, and advantageswill be more fully understood upon reading the following detaileddescription in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is a block diagram illustrating one embodiment of an environmentin which the present systems and methods may be implemented;

FIG. 2 is a block diagram illustrating one example of a entity detectionmodule;

FIG. 3 is a block diagram illustrating one example of an environment fordetecting an entity at a predefined area;

FIG. 4 is a block diagram illustrating another example of an environmentfor detecting an entity at a predefined area;

FIG. 5 is a flow diagram illustrating one embodiment of a method fordetecting an entity at a predefined area;

FIG. 6 is a flow diagram illustrating one embodiment of a method forgenerating a notification upon satisfying a predetermined condition;

FIG. 7 is a flow diagram illustrating one embodiment of a method fortracking a person relative to a vehicle; and

FIG. 8 depicts a block diagram of a computer system suitable forimplementing the present systems and methods.

While the embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION

The systems and methods described herein relate to home automation. Morespecifically, the systems and methods described herein relate to entitydetection in relation to a home automation system. In one example, auser may desire to receive an alert when someone enters a predefinedarea. For instance, a shop owner may want to know when a person entersor leaves through a doorway. Presently, a sensor may detect when anobject disrupts a beam sensor (e.g., infrared (IR) beam sensor, etc.)associated with the door of the shop. Upon detecting an object passingthrough the IR beam, a chime may sound. The shop owner, however, mayhave to actively monitor the shop to know whether a person has passedthrough the beam sensor, or to know how many people have entered and/orexited the shop. The present systems and methods detect when a personpasses through a predetermined perimeter and generate a notificationupon detecting one or more conditions being satisfied in relation todetecting a person passing through the predetermined perimeter.Moreover, the systems and methods described herein may provide entitydetection, customer recognition, object recognition, queryingcustomer-related data in relation to recognizing the customer, andgenerating notifications in relation to detecting an entity and/oridentifying the type of entity (e.g., vehicles, humans, animals, etc.).

FIG. 1 is a block diagram illustrating one embodiment of an environment100 in which the present systems and methods may be implemented. In someembodiments, the systems and methods described herein may be at leastpartially performed on a device (e.g., device 105). The environment 100may include a device 105, server 110, a sensor 125, a display 130, amobile computing device 150, a home automation controller 155, and anetwork 115 that allows the device 105, the server 110, the mobilecomputing device 150, home automation controller 155, and sensor 125 tocommunicate with one another. Examples of the device 105 include mediacontent set top box, satellite set top box, cable set top box, DVRs,personal video recorders (PVRs), mobile devices, smart phones, personalcomputing devices, computers, servers, etc. Examples of the homeautomation controller 155 include a dedicated home automation computingdevice (e.g., wall-mounted controller), a personal computing device(e.g., laptop, desktop, etc.), a mobile computing device (e.g., tabletcomputing device, smartphone, etc.), and the like.

Examples of sensor 125 include a camera sensor, audio sensor, proximitysensor, boundary sensor, light beam sensor, three-dimensional (3-D)sensor, motion sensor, door sensor, window sensor, accelerometer, globalpositioning system (GPS) sensor, Wi-Fi positioning system sensor,capacitance sensor, radio frequency sensor, near-field sensor, voicesensor, and the like. Sensor 125 may represent one or more separatesensors or a combination of two or more sensors in a single device. Forexample, sensor 125 may represent one or more camera sensors and one ormore motion sensors connected to environment 100. Additionally, oralternatively, sensor 125 may represent a combination sensor such asboth a camera sensor and a motion sensor integrated in the same device.Sensor 125 may be integrated with a facial recognition system. Althoughsensor 125 is depicted as connecting to device 105 over network 115, insome embodiments, sensor 125 may connect directly to device 105.Additionally, or alternatively, sensor 125 may be integrated with a homeappliance or fixture such as a light bulb fixture. Sensor 125 mayinclude an accelerometer to enable sensor 125 to detect a movement. Forexample, sensor 125 may be attached to an item in a grocery storecarried by a patron of the grocery store (e.g., shopping cart, item tobe purchased, etc.). Sensor 125 may include a wireless communicationdevice enabling sensor 125 to send and receive data and/or informationto and from one or more devices in environment 100. Additionally, oralternatively, sensor 125 may include a GPS sensor to enable sensor 125to track a location of sensor 125. Sensor 125 may include a proximitysensor to enable sensor to detect a proximity of a person relative to anobject to which the sensor is attached and/or associated.

In some configurations, the device 105 may include a user interface 135,application 140, and entity detection module 145. Although thecomponents of the device 105 are depicted as being internal to thedevice 105, it is understood that one or more of the components may beexternal to the device 105 and connect to device 105 through wiredand/or wireless connections. In some embodiments, application 140 may beinstalled on mobile computing device 150 in order to allow a user tointerface with a function of device 105, entity detection module 145,home automation controller 155, and/or server 110.

In some embodiments, device 105 may communicate with server 110 vianetwork 115. Examples of networks 115 include cloud networks, local areanetworks (LAN), wide area networks (WAN), virtual private networks(VPN), wireless networks (using 802.11, for example), cellular networks(using 3G and/or LTE, for example), Z-Wave networks, etc. In someconfigurations, the network 115 may include the internet. It is notedthat in some embodiments, the device 105 may not include a entitydetection module 145. For example, device 105 may include application140 that allows device 105 to interface with home automation controller155 via entity detection module 145 located on another device such asmobile computing device 150 and/or server 110.

In some embodiments, device 105, home automation controller 155, andserver 110 may include a entity detection module 145 where at least aportion of the functions of entity detection module 145 are performedseparately and/or concurrently on device 105, home automation controller155, and/or server 110. Likewise, in some embodiments, a user may accessthe functions of device 105 and/or home automation controller 155(directly or through device 105 via entity detection module 145) frommobile computing device 150. For example, in some embodiments, mobilecomputing device 150 includes a mobile application that interfaces withone or more functions of device 105, home automation controller 155,entity detection module 145, and/or server 110. In some embodiments, atleast a portion of the functions of the entity detection module 145 mayexecute on one or more devices located in a cloud network, including anyone of the devices illustrated in FIG. 1. For example, one or morecomputing devices may connect to and communicate with a home automationcontroller in a home over a cloud network. Thus, one or more functionsof the entity detection module 145 may be performed on one or moredevices in a cloud network. Performance of the function by the one ormore devices in the cloud network may include interacting, controllingan aspect of, and/or communicating with one or more devices in the homevia the home automation controller.

In some embodiments, server 110 may be coupled to database 120. Database120 may include entity data 160 and other information related to apredefined area. For example, device 105 may access entity data 160 indatabase 120 over network 115 via server 110. Database 120 may beinternal or external to the server 110. In one example, device 105 maybe coupled directly to database 120, database 120 being internal orexternal to device 105. Entity data may include information regarding anidentifier assigned to an entity, information regarding entity types(e.g., person, vehicle, animal, etc.), detected features of entities,feature detection signatures, transaction data (e.g., transactionamount, merchandise purchased, average transactions per month, totalnumber of transactions, etc.), average time an entity spends at apredefined area (e.g., time spent in a shop, at a restaurant, abusiness, etc.).

Entity detection module 145 may allow a user to track an entity in apredefined space. For example, entity detection module 145 may track avehicle in a parking lot. Additionally, or alternatively, entitydetection module 145 may track a user exiting the vehicle, entering apredefined area (e.g., a place of business), and logging informationregarding a visit by the detected entity. Entities may include bothanimate objects such as persons and animals, inanimate objects under thecontrol of animate objects such as modes of transportation (e.g.,vehicles, buses, trains, motorbikes, bicycles, etc.), and simplyinanimate objects.

FIG. 2 is a block diagram illustrating one example of a entity detectionmodule 145-a. Entity detection module 145-a may be one example of entitydetection module 145 depicted in FIG. 1. As depicted, entity detectionmodule 145-a may include camera module 205, classification module 210,recognition module 215, identifier module 220, notification module 225,and tracking module 230.

In one embodiment, the camera module 205 may detect, in conjunction witha camera, an entity passing through a perimeter of a predefined area.The predefined area may include a shop, a restaurant, a building, anarea of the building, a home, an area of the home, a parking lot, ananimal cage, a wilderness area, and the like. Upon detecting the entitypassing through the perimeter of the predefined area, classificationmodule 210 may classify a type of the entity from an image of the entitycaptured by the camera. Several different types of entities may beregistered in a database. Types may include a vehicle, a human, ananimal, animate objects, inanimate objects, etc. The types may includesub-types such as sub-types of vehicles (e.g., car, truck, sedan, coupe,make, model, etc.), sub-types of humans (e.g., child, teenager, adult,senior adult, etc.), sub-types of animals (e.g., dog, cat, sheep, wolf,etc.), and so forth. Upon classifying the type of the entity,recognition module 215 may detect one or more features of the entityfrom the image of the entity. The detected features may be used by thesystem to distinguish one entity from another. In some cases, identifiermodule 220 may assign an identifier to the entity based on the type andthe detected feature of the entity. The identifier may be used todistinguish the entity from another entity of a same type. For example,one person standing in a shop may be assigned a first identifier and asecond person standing in the shop may be assigned a second identifier,where both the first and second identifiers are identifiers associatedwith people. Additionally, or alternatively, the identifier may be usedto store information regarding the visit of the entity in a database.

In some embodiments, an entry in the database may store this informationand may include the identifier. Thus, information regarding the entitymay be queried and retrieved based on a search using the assignedidentifier. The identifier may include a string of characters that thesystem uses to store information associated with the entity. In order toaddress privacy concerns, in some embodiments, the data associated witha human-type entity (e.g., a person) may exclude information thatreveals the actual identity of the entity such as name, address,telephone number, credit card number, social security number, and anyother contact information related to the actual identity of the person.

Upon detecting the entity, notification module 225 may generate anotification indicating information regarding the detection of theentity. For example, the classification module 210 may classify oneentity as a person and a second entity as an animal. For example, aveterinarian may receive a first notification indicating that a personhas entered the clinic, and may receive a second notification indicatingthat a second person has entered the clinic with an animal in tow.

Tracking module 230 may determine how many entities of the same type asthe detected entity are located in the predefined area. For example,tracking module may determine that five persons remain in an area of ashop. Upon detecting the number of entities in the predefined area,notification module 225 may generate a notification indicating thenumber of entities in the predefined area. Tracking module 230 maydetermine a length of stay of the entity at the predefined area based ona detected time of arrival of the entity at the predefined area and adetected time of departure of the entity from the predefined area.

In one embodiment, recognition module 215 may recognize the entity,based on the detected feature of the entity. For example, recognitionmodule 215 may recognize a visual feature (e.g., facial recognition,etc.), an audio feature (e.g., voice recognition), a character feature(character recognition identifying characters on a license plate, forexample), a device identifier of a mobile device predetermined to beassociated with the entity, and the like.

Upon recognizing the entity, notification module 225 may generate anotification indicating the recognized entity. For example, a person maywant to know when a particular person arrives at a certain area, such asa preferred customer arriving at a shop. In some embodiments, identifiermodule 220 may determine whether the recognized entity was assigned anidentifier on a previous visit to the predefined area. If the entity waspreviously assigned an identifier, then information regarding thecurrent visit of the entity may be stored in a database entry associatedwith the previously assigned identifier. Thus, the detected feature ofthe entity may be compared to one or more predefined features stored ina database. Upon matching the detected feature to one or more of thepredefined features stored in the database, the identifier module 220may determine whether the entity was previously assigned an identifier.Previously acquired data and information regarding the recognized entitymay be stored in a database entry according to an identifier previouslyassigned to the entity.

In one embodiment, tracking module 230 may determine an average lengthof stay of the entity at the predefined area and a previously determinedlength of stay determined from the previous visit by the entity to thepredefined area. Upon determining the entity was assigned an identifieron a previous visit, tracking module 230 may determine how many previousvisits the entity has made to the predefined area. Upon determining howmany previous visits the entity has made to the predefined area,tracking module 230 may determine an average rate of visits over apredetermined time period. Upon determining information regarding visitsmade by the entity, notification module 225 may generate a notificationindicating information regarding the visits.

In some embodiments, tracking module 230 may associate, with theidentifier of the entity, a transaction performed by the entity at thepredefined area. Thus, transactions of a person at a store may betracked and associated with particular customer. As explained above,privacy concerns may be addressed by storing only non-identifyinginformation regarding the entity, where the assigned identifier is astring of characters generated by the system and not based on privateinformation of the entity (e.g., name, address, phone number, creditcard number, social security number, driver license number, etc.).Tracking module 230 may determine an average value of transactions fromthe transaction and previous transactions associated with the identifierof the entity.

In one example, classification module 205 may identify the type of theentity as a vehicle, the predefined area being a parking lot.Classification module 205 may identify a type of a second entity exitingthe vehicle as a human. Tracking module 230 may track a location of thesecond entity, i.e., the person exiting the vehicle. Tracking module 230may track where the person goes after exiting the vehicle. For example,tracking module 230 may determine whether the person enters a particularshop after parking in the parking lot of the shop. The parking lot mayinclude signs that are posted to provide notification that onlycustomers of this particular shop may park in the parking lot. Upondetermining that the person does not patronize the shop after parking inthis parking lot, notification module 225 may generate a notificationalerting the shop that a non-customer is parked in their parking lot.

FIG. 3 is a block diagram illustrating one example of an environment 300for detecting an entity at a predefined area 305. As depicted, thepredefined area 305 may include a person 310, a camera 315, a datatransceiver 320, and a vehicle 325. Camera 315 may communicate withentity detection module 145 via data transceiver 320.

In some embodiments, predefined area 305 may include a parking area suchas a parking lot or parking garage. For example, camera 315, inconjunction with entity detection module 145, may detect an entityarriving at a parking area. The entity may be identified as a vehicle.Thus, in addition to detecting the vehicle, the one or more entitiesexiting the vehicle may be identified as people.

As described above, entity detection module 145 may allow a user tocount the number of entities within a predefined area. Thus, camera 315may allow entity detection module 145 to count the number of vehicles ina parking area. Additionally, or alternatively, camera 315 may allowentity detection module 145 to count the number of persons in apredefined area.

FIG. 4 is a block diagram illustrating another example of an environment400 for detecting an entity at a predefined area 405. Environment 400may include a predefined area 405, a person 410, a camera 415, and datatransceiver 420. Camera 415 may communicate with entity detection module145 via data transceiver 420.

In some embodiments, predefined area 405 may include a place of businesssuch as a restaurant, a shop, a waiting area of a customer, client,and/or patient, etc. For example, camera 415, in conjunction with entitydetection module 145, may detect an entity arriving at shop. Thearriving entity may be identified as a person. Camera 415 may allowentity detection module 145 to count the number of persons in the shop.As described above, the person may purchase an item from the shop.Having assigned an identifier to the person, data regarding thetransaction by the person may be recorded in a database. Additionally,or alternatively, the assigned identifier, identifying characteristics,and/or data regarding the visit may be recorded in a database. Thus, inone example, entity detection module 145 may detect the person arrivingin a vehicle. An identifying characteristic of the vehicle may bedetected (e.g., model, make, license plate number, etc.), and it may bedetermined that the vehicle has already been assigned an identifier.Accordingly, information regarding earlier visits may be queried. It maybe determined that the person is a preferred customer. A shop owner mayreceive a notification regarding the arrival of a preferred customer. Inanother example, the data may indicate that the user on a previous visitparked in the shop's private parking lot, but never patronized the shop,despite posted signs that indicate that the parking is only forcustomers of the shop. Accordingly, the shop owner may receivenotifications regarding the arrival of a non-patronizing user of theparking area.

FIG. 5 is a flow diagram illustrating one embodiment of a method 500 fordetecting an entity at a predefined area. In some configurations, themethod 500 may be implemented by the entity detection module 145illustrated in FIGS. 1 and/or 2. In some configurations, the method 500may be implemented in conjunction with the application 140 and/or theuser interface 135 illustrated in FIG. 1.

At block 505, an entity passing through a perimeter of a predefined areamay be detected via a camera. At block 510, upon detecting the entitypassing through the perimeter of the predefined area, a type of theentity may be classified from an image of the entity captured by thecamera. At block 515, upon classifying the type of the entity, a featureof the entity may be detected from the image of the entity. At block520, an identifier may be assigned to the entity based on the type andthe detected feature of the entity. The identifier may distinguish theentity from another entity of a same type.

FIG. 6 is a flow diagram illustrating one embodiment of a method 600 forgenerating a notification upon satisfying a predetermined condition. Insome configurations, the method 600 may be implemented by the entitydetection module 145 illustrated in FIGS. 1 and/or 2. In someconfigurations, the method 600 may be implemented in conjunction withthe application 140 and/or the user interface 135 illustrated in FIG. 1.

At block 605, an entity passing through a perimeter of a predefined areamay be detected via a camera. At block 610, upon classifying the type ofthe entity, it may be determined how many entities of the same type asthe entity are located in the predefined area. At block 615, anotification indicating the number of entities located in the predefinedarea may be generated. At block 620, the entity may be recognized basedon a detected feature of the entity (e.g., facial recognition, voicerecognition, etc.). At block 625, a transaction initiated by the entityat the predefined area may be associated with the entity.

FIG. 7 is a flow diagram illustrating one embodiment of a method 700 fortracking a person relative to a vehicle. In some configurations, themethod 700 may be implemented by the entity detection module 145illustrated in FIGS. 1 and/or 2. In some configurations, the method 700may be implemented in conjunction with the application 140 and/or theuser interface 135 illustrated in FIG. 1.

At block 705, the type of the entity may be identified as a vehicle. Atblock 710, a type of a second entity exiting the vehicle may beidentified as a human. At block 715, a location of the second entity maybe tracked. At block 720, it may be determined whether the second entityenters a second predefined area. At block 725, a notification may begenerated in response to detecting the second entity not entering thesecond predefined area.

FIG. 8 depicts a block diagram of a controller 800 suitable forimplementing the present systems and methods. The controller 800 may bean example of the set top box device 105, mobile computing device 150,and/or home automation controller 155 illustrated in FIG. 1. In oneconfiguration, controller 800 includes a bus 805 which interconnectsmajor subsystems of controller 800, such as a central processor 815, asystem memory 820 (typically RAM, but which may also include ROM, flashRAM, or the like), an input/output controller 825, an external audiodevice, such as a speaker system 830 via an audio output interface 835,an external device, such as a display screen 835 via display adapter840, an input device 845 (e.g., remote control device interfaced with aninput controller 850), multiple USB devices 865 (interfaced with a USBcontroller 870), and a storage interface 880. Also included are at leastone sensor 855 connected to bus 805 through a sensor controller 860 anda network interface 885 (coupled directly to bus 805).

Bus 805 allows data communication between central processor 815 andsystem memory 820, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM is generally the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components or devices. For example, the entity detectionmodule 145-b to implement the present systems and methods may be storedwithin the system memory 820. Applications (e.g., application 140)resident with controller 800 are generally stored on and accessed via anon-transitory computer readable medium, such as a hard disk drive(e.g., fixed disk 875) or other storage medium. Additionally,applications can be in the form of electronic signals modulated inaccordance with the application and data communication technology whenaccessed via interface 885.

Storage interface 880, as with the other storage interfaces ofcontroller 800, can connect to a standard computer readable medium forstorage and/or retrieval of information, such as a fixed disk drive 875.Fixed disk drive 875 may be a part of controller 800 or may be separateand accessed through other interface systems. Network interface 885 mayprovide a direct connection to a remote server via a direct network linkto the Internet via a POP (point of presence). Network interface 885 mayprovide such connection using wireless techniques, including digitalcellular telephone connection, Cellular Digital Packet Data (CDPD)connection, digital satellite data connection, or the like. In someembodiments, one or more sensors (e.g., motion sensor, smoke sensor,glass break sensor, door sensor, window sensor, carbon monoxide sensor,and the like) connect to controller 800 wirelessly via network interface885.

Many other devices or subsystems (not shown) may be connected in asimilar manner (e.g., entertainment system, computing device, remotecameras, wireless key fob, wall mounted user interface device, cellradio module, battery, alarm siren, door lock, lighting system,thermostat, home appliance monitor, utility equipment monitor, and soon). Conversely, all of the devices shown in FIG. 8 need not be presentto practice the present systems and methods. The devices and subsystemscan be interconnected in different ways from that shown in FIG. 8. Theaspect of some operations of a system such as that shown in FIG. 8 arereadily known in the art and are not discussed in detail in thisapplication. Code to implement the present disclosure can be stored in anon-transitory computer-readable medium such as one or more of systemmemory 820 or fixed disk 875. The operating system provided oncontroller 800 may be iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®,UNIX®, LINUX®, or another known operating system.

Moreover, regarding the signals described herein, those skilled in theart will recognize that a signal can be directly transmitted from afirst block to a second block, or a signal can be modified (e.g.,amplified, attenuated, delayed, latched, buffered, inverted, filtered,or otherwise modified) between the blocks. Although the signals of theabove described embodiment are characterized as transmitted from oneblock to the next, other embodiments of the present systems and methodsmay include modified signals in place of such directly transmittedsignals as long as the informational and/or functional aspect of thesignal is transmitted between blocks. To some extent, a signal input ata second block can be conceptualized as a second signal derived from afirst signal output from a first block due to physical limitations ofthe circuitry involved (e.g., there will inevitably be some attenuationand delay). Therefore, as used herein, a second signal derived from afirst signal includes the first signal or any modifications to the firstsignal, whether due to circuit limitations or due to passage throughother circuit elements which do not change the informational and/orfinal functional aspect of the first signal.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexemplary in nature since many other architectures can be implemented toachieve the same functionality.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

Furthermore, while various embodiments have been described and/orillustrated herein in the context of fully functional computing systems,one or more of these exemplary embodiments may be distributed as aprogram product in a variety of forms, regardless of the particular typeof computer-readable media used to actually carry out the distribution.The embodiments disclosed herein may also be implemented using softwaremodules that perform certain tasks. These software modules may includescript, batch, or other executable files that may be stored on acomputer-readable storage medium or in a computing system. In someembodiments, these software modules may configure a computing system toperform one or more of the exemplary embodiments disclosed herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the present systems and methods and their practicalapplications, to thereby enable others skilled in the art to bestutilize the present systems and methods and various embodiments withvarious modifications as may be suited to the particular usecontemplated.

Unless otherwise noted, the terms “a” or “an,” as used in thespecification and claims, are to be construed as meaning “at least oneof” In addition, for ease of use, the words “including” and “having,” asused in the specification and claims, are interchangeable with and havethe same meaning as the word “comprising.” In addition, the term “basedon” as used in the specification and the claims is to be construed asmeaning “based at least upon.”

What is claimed is:
 1. A method for entity detection by a processor,comprising: detecting an entity entering a predefined area based atleast in part on camera data, wherein the entity includes a human or ananimal; detecting a feature of the entity based at least in part on thecamera data; comparing the detected feature to one or more predefinedfeatures stored in a database; matching the detected feature to one ormore of the predefined features; recognizing the entity based at leastin part on an image of the entity captured by a camera and the detectedfeature; determining an identifier assigned to the entity based at leastin part on matching the detected feature to one or more of thepredefined features, wherein the identifier was assigned to the entityat a previous time; and generating a notification indicating the entitybased at least in part on determining the identifier.
 2. The method ofclaim 1, wherein the feature of the entity is a facial feature, an audiofeature, or a device identifier of a mobile device associated with theentity.
 3. The method of claim 1, further comprising: associating, withthe identifier of the entity, a transaction by the entity at thepredefined area; and determining an average value of transaction fromthe transaction and previous transactions associated with the identifierof the entity, wherein the notification is based at least in part on theaverage value.
 4. The method of claim 1, wherein the predefined areaincludes a shop, a restaurant, a home, an area of the home, an animalcage, or a wilderness area.
 5. The method of claim 1, furthercomprising: detecting that a vehicle is positioned in a parking lotbased at least in part on second camera data; identifying the entityexiting the vehicle based at least in part on the second camera data;and tracking a location of the entity, wherein detecting the entityentering the predefined area is based at least in part on tracking thelocation of the entity.
 6. The method of claim 5, further comprising:determining an average length of stay of the entity at the predefinedarea based at least in part on at least one length of a previous stay ofthe entity at the predefined area, wherein the notification is based atleast in part on the average length of stay of the entity.
 7. The methodof claim 5, wherein tracking the location of the entity furthercomprises: tracking the location of the entity relative to the parkinglot for a predetermined length of time since an arrival of the vehiclein the parking lot, wherein the notification is based at least in parton a time since the arrival of the vehicle exceeding the predeterminedlength of time and the tracking.
 8. The method of claim 1, furthercomprising: classifying a type of the entity based at least in part onthe image of the entity captured by a camera, wherein recognizing theentity is based at least in part on the type of the entity.
 9. Anapparatus for entity detection, comprising: a processor; memory inelectronic communication with the processer; and instructions stored inthe memory and operable, when executed by the processor, to cause theapparatus to: detect an entity entering a predefined area based at leastin part on camera data, wherein the entity includes a human or ananimal; detect a feature of the entity based at least in part on thecamera data; compare the detected feature to one or more predefinedfeatures stored in a database; match the detected feature to one or moreof the predefined features; recognize the entity based at least in parton an image of the entity captured by a camera and the detected feature;determine an identifier assigned to the entity based at least in part onmatching the detected feature to one or more of the predefined features,wherein the identifier was assigned to the entity at a previous time;and generate a notification indicating the entity based at least in parton determining the identifier.
 10. The apparatus of claim 9, wherein thefeature of the entity is a facial feature, an audio feature, or a deviceidentifier of a mobile device associated with the entity.
 11. Theapparatus of claim 9, wherein the instructions are further executable bythe processor to: associating, with the identifier of the entity, atransaction by the entity at the predefined area; and determining anaverage value of transaction from the transaction and previoustransactions associated with the identifier of the entity, wherein thenotification is based at least in part on the average value.
 12. Anon-transitory computer readable medium storing code for entitydetection, the code comprising instructions executable by a processorto: detect an entity entering a predefined area based at least in parton camera data, wherein the entity includes a human or an animal; detecta feature of the entity based at least in part on the camera data;compare the detected feature to one or more predefined features storedin a database; match the detected feature to one or more of thepredefined features; recognize the entity based at least in part on animage of the entity captured by a camera and the detected feature;determine an identifier assigned to the entity based at least in part onmatching the detected feature to one or more of the predefined features,wherein the identifier was assigned to the entity at a previous time;and generate a notification indicating the entity based at least in parton determining the identifier.